Papers by Dilshod Azizov
L4: Mutual Learning Helps Lifelong Language Learning (2025.emnlp-industry)
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| Challenge: | Existing distillation methods rely on domain-specific teachers, limiting their ability to update in real-time and adapt to dynamic environments. |
| Approach: | They propose a framework that enables continuous mutual learning from task streams without relying on domain-specific teachers. |
| Outcome: | The proposed framework reduces catastrophic forgetting while improving performance on various benchmark datasets making it suitable for real-world, dynamic natural language processing (NLP) applications. |
AICD Bench: A Challenging Benchmark for AI-Generated Code Detection (2026.eacl-long)
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| Challenge: | Existing benchmarks for detecting AI-generated code are limited to binary human–machine classification under in-distribution settings. |
| Approach: | They propose to use AICD Bench to build a robust binary classification framework for large language models. |
| Outcome: | The proposed benchmark spans 2M examples, 77 models across 11 families, and 9 programming languages. |
SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles (2024.findings-emnlp)
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| Challenge: | a new corpus of news media and articles is developed to assess political bias and factuality in cross-lingual contexts . integrity and objectivity of news are crucial in an age of information sharing across cultural and language landscapes - a recent study shows . |
| Approach: | They propose a corpus of news media and articles for predicting political bias and factuality . they evaluate the cross-lingual ability of the models; however, they evaluate on English data . |
| Outcome: | The proposed corpus is unprecedented in its collection and evaluates on English data. |
MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media (2025.naacl-long)
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| Challenge: | Existing methods for profiling news media focus on textual features, causing them to overlook complex relationships between entities. |
| Approach: | They propose a framework for profiling news media from the lens of political bias and factuality. |
| Outcome: | The proposed framework improves existing models and improves them by integrating structural information from similar nodes. |
A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis (2026.findings-acl)
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Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, Preslav Nakov
| Challenge: | a large-scale label set for media outlets from Media Bias/Fact Check (MBFC) is lacking in the field. |
| Approach: | They propose to use a large-scale label set to analyze outlets' representations . they also propose to evaluate embedding views and fusion strategies . |
| Outcome: | The proposed method achieves state-of-the-art results on ACL-2020 and establishes strong benchmarks on MBFC-2025. |
CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have revolutionized code generation but have significant consequences for programming skills, ethics, and assessment integrity. |
| Approach: | They propose a framework capable of distinguishing between human-written and LLM-generated program code across multiple programming languages, code generators, and domains. |
| Outcome: | The proposed framework distinguishes between human-written and LLM-generated program code across multiple programming languages, code generators, and domains. |
Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts (2025.findings-acl)
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| Challenge: | Important efforts to characterize news media outlets in terms of their political bias and factuality are labor-intensive and prone to human biases. |
| Approach: | They propose a method that emulates criteria used by professional fact-checkers to assess the factuality and political bias of an entire outlet. |
| Outcome: | The proposed method improves on baselines and with multiple LLMs. |